Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering

TL;DR

This paper introduces a novel local gradient-aware surface filtering method for estimating normals in noisy 3D point clouds, achieving state-of-the-art results in normal estimation, surface reconstruction, and denoising.

Contribution

It proposes a new implicit surface filtering approach that incorporates local gradient constraints to improve normal estimation from noisy point clouds.

Findings

Achieves superior accuracy in normal estimation on noisy data.

Enhances surface reconstruction quality from noisy point clouds.

Effectively denoises point clouds while preserving surface details.

Abstract

Estimating normals for noisy point clouds is a persistent challenge in 3D geometry processing, particularly for end-to-end oriented normal estimation. Existing methods generally address relatively clean data and rely on supervised priors to fit local surfaces within specific neighborhoods. In this paper, we propose a novel approach for learning normals from noisy point clouds through local gradient-aware surface filtering. Our method projects noisy points onto the underlying surface by utilizing normals and distances derived from an implicit function constrained by local gradients. We start by introducing a distance measurement operator for global surface fitting on noisy data, which integrates projected distances along normals. Following this, we develop an implicit field-based filtering approach for surface point construction, adding projection constraints on these points during filtering. To address issues of over-smoothing and gradient degradation, we further incorporate local gradient consistency constraints, as well as local gradient orientation and aggregation. Comprehensive experiments on normal estimation, surface reconstruction, and point cloud denoising demonstrate the state-of-the-art performance of our method. The source code and trained models are available at https://github.com/LeoQLi/LGSF.